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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Ensuring solution quality	28%	- Reliability and performance 1. Fault tolerance and recovery strategies 2. Monitoring pipelines and workloads - Security and governance 1. Data governance and compliance 2. IAM and access control in GCP
Operationalizing machine learning models	26%	- ML pipeline integration 1. Feature engineering and feature stores 2. Vertex AI pipeline deployment - Model deployment and monitoring 1. Model monitoring and drift detection 2. Online vs batch prediction
Designing data processing systems	22%	- Data architecture and storage design 1. Designing scalable and cost-effective data models 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse) - Batch and streaming data processing design 1. Latency, throughput, and consistency trade-offs 2. Event-driven vs batch architectures
Building and operationalizing data processing systems	24%	- Data ingestion and integration 1. Batch ingestion pipelines (BigQuery, Cloud Storage) 2. Streaming ingestion (Pub/Sub, Dataflow) - Data processing and transformation 1. ETL/ELT pipeline design 2. Using Dataproc, Dataflow, and BigQuery SQL

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Google Certified Professional Data Engineer Exam Sample Questions (Q294-Q299):

NEW QUESTION # 294
Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

A. Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.
B. Create a Hadoop cluster on Google Compute Engine that uses persistent disks.
C. Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
D. Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.
E. Create a Google Cloud Dataflow job to process the data.

Answer: C

Explanation:
Dataproc is used to migrate Hadoop and Spark jobs on GCP. Dataproc with GCS connected through Google Cloud Storage connector helps store data after the life of the cluster. When the job is high I/O intensive, then we need to create a small persistent disk.

NEW QUESTION # 295
Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

A. Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.
B. Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.
C. Tune the Cloud Dataproc cluster so that there is just enough disk for all data.
D. Put the data into Google Cloud Storage.

Answer: A

Explanation:
References:

NEW QUESTION # 296
Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
* Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads
* Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
* Databases
* 8 physical servers in 2 clusters
* SQL Server - user data, inventory, static data
* 3 physical servers
* Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
* Application servers - customer front end, middleware for order/customs
* 60 virtual machines across 20 physical servers
* Tomcat - Java services
* Nginx - static content
* Batch servers
Storage appliances
* iSCSI for virtual machine (VM) hosts
* Fibre Channel storage area network (FC SAN) - SQL server storage
* Network-attached storage (NAS) image storage, logs, backups
* 10 Apache Hadoop /Spark servers
* Core Data Lake
* Data analysis workloads
* 20 miscellaneous servers
* Jenkins, monitoring, bastion hosts,
Business Requirements
* Build a reliable and reproducible environment with scaled panty of production.
* Aggregate data in a centralized Data Lake for analysis
* Use historical data to perform predictive analytics on future shipments
* Accurately track every shipment worldwide using proprietary technology
* Improve business agility and speed of innovation through rapid provisioning of new resources
* Analyze and optimize architecture for performance in the cloud
* Migrate fully to the cloud if all other requirements are met
Technical Requirements
* Handle both streaming and batch data
* Migrate existing Hadoop workloads
* Ensure architecture is scalable and elastic to meet the changing demands of the company.
* Use managed services whenever possible
* Encrypt data flight and at rest
* Connect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

A. Create a view on the table to present to the virtualization tool.
B. Create an additional table with only the necessary columns.
C. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
D. Export the data into a Google Sheet for virtualization.

Answer: A

NEW QUESTION # 297
Your company's data platform ingests CSV file dumps of booking and user profile data from upstream sources into Cloud Storage. The data analyst team wants to join these datasets on the email field available in both the datasets to perform analysis. However, personally identifiable information (PII) should not be accessible to the analysts. You need to de-identify the email field in both the datasets before loading them into BigQuery for analysts. What should you do?

A. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.2.
Create a policy tag with the email mask as the data masking rule.3. Assign the policy to the email field in both tables. A4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.
B. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type.2. Load the booking and user profile data into a BigQuery table.
C. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.2.
Create a policy tag with the default masking value as the data masking rule.3. Assign the policy to the email field in both tables.4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts
D. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data Loss Prevention (Cloud DLP) with masking as the de-identification transformations type.2. Load the booking and user profile data into a BigQuery table.

Answer: B

Explanation:
Cloud DLP is a service that helps you discover, classify, and protect your sensitive data. It supports various de-identification techniques, such as masking, redaction, tokenization, and encryption. Format-preserving encryption (FPE) with FFX is a technique that encrypts sensitive data while preserving its original format and length. This allows you to join the encrypted data on the same field without revealing the actual values. FPE with FFX also supports partial encryption, which means you can encrypt only a portion of the data, such as the domain name of an email address. By using Cloud DLP to de-identify the email field with FPE with FFX, you can ensure that the analysts can join the booking and user profile data on the email field without accessing the PII. You can create a pipeline to de-identify the email field by usingrecordTransformations in Cloud DLP, which allows you to specify the fields and the de-identification transformations to apply to them.
You can then load the de-identified data into a BigQuery table for analysis. References:
De-identify sensitive data | Cloud Data Loss Prevention Documentation
Format-preserving encryption with FFX | Cloud Data Loss Prevention Documentation De-identify and re-identify data with the Cloud DLP API De-identify data in a pipeline

NEW QUESTION # 298
You have data pipelines running on BigQuery, Cloud Dataflow, and Cloud Dataproc. You need to perform health checks and monitor their behavior, and then notify the team managing the pipelines if they fail. You also need to be able to work across multiple projects. Your preference is to use managed products of features of the platform. What should you do?

A. Run a Virtual Machine in Compute Engine with Airflow, and export the information to Stackdriver
B. Export the information to Cloud Stackdriver, and set up an Alerting policy
C. Develop an App Engine application to consume logs using GCP API calls, and send emails if you find a failure in the logs
D. Export the logs to BigQuery, and set up App Engine to read that information and send emails if you find a failure in the logs

Answer: B

Explanation:
Monitoring does not only provide you with access to Dataflow-related metrics, but also lets you to create alerting policies and dashboards so you can chart time series of metrics and choose to be notified when these metrics reach specified values.

NEW QUESTION # 299
......

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